What are some of the most popular data science tools, how do you use them, and what are their features? In this course, you'll learn about Jupyter Notebooks, RStudio IDE, Apache Zeppelin and Data Science Experience. You will learn about what each tool is used for, what programming languages they can execute, their features and limitations. With the tools hosted in the cloud on Cognitive Class Labs, you will be able to test each tool and follow instructions to run simple code in Python, R or Scala. To end the course, you will create a final project with a Jupyter Notebook on IBM Data Science Experience and demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.
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강사:

Polong Lin

Data Scientist

스크립트

[SOUND] Welcome to RStudio IDE, environment and history. As you see here, the top right quadrant of Rtudio IDE contains the Environment and History tabs. The Environment tab contains a list of all the variables you've used in your R code. So when we said, x is equal to 1 in the console, the variable x and its current value displays in this tab. This is a good way to quickly see and keep track of all the variables you've defined. Note that the SC environment variable is for Apache Spark, which is pre-installed for you. If you don't need it or want to remove it, you can do so from the environment using the command rm, as in rm with sc within parentheses. Another interesting feature within RStudio IDE is that it includes built in datasets, which in R are called a data frame. An example of such a data set is MTCars, which contains data on the latest motor trends. So if you were to type in mtcars in the R editor and run it, the output would display in tabular form, as shown here in the console. There are other built-in data sets, and you can run the data command with a set of md parenthesis to see the full list. For example, you can type in the first one, air passengers, and run it to see the contents. Now let's look at what's available and what's displayed on these two tabs and how to navigate in them. If you want to save all the variables in your R environment, you can click on the save button to save your workspace to disk as an R data file. This way, if you load the R data file, it'll load all of your saved variables and data frames back into R. You can also import your own data sets by clicking on Import Data Set. The Import Data Set allows you to load files from your local drive or from a web URL. And the last icon allows you to delete all objects from the current environment. Doing this as useful if you want to clean up your environment and start from an empty workspace again. However, please be careful and use discretion with this action because it can not be undone. The Global Environment's drop-down allows you to look at all the packages that are currently loaded in your environment. When you click on the History tab, you'll be able to see all the code you've executed, but only in this session either from the console or from the source location of the data. You can double click on an entry to paste it into console. This ends the video on environment and history. We encourage you to take some time to practice what you've learned, as well as doing the lab. Thanks for watching. [SOUND]